Several years ago I took a class with Richard Zekchauser that, in short, helped reformulate a lot of the way I think about uncertainty in the world. For example, imagine trying to find the answer to a question "how many homeless children live in Katmandu," or, imagine you were grading a 50 question multiple choice test for 20 students, but didn't have the grading rubric? How would you most accurately grade the test? Everything is about uncertainty and cleverness, and this book was written by Richard's mentor Howard Raiffa - a/the father of decision analysis and an early (aggressive) proponent of Bayesian perspectives in statistics. This book was SUPERB if and only if you want an intense mathematical dive into decision analysis (you probably don), but if you do READ!!!
A few things I learned.
1. The practice of outlining subjective probability assessments of a given scenario is both useful, and a skill, and people are SHOCKINGLY bad at it. I've been trying to make 90% confidence around things recently and I miss SO much (wayyy more than 10% of the time)
2. Contemplating if more information actually changes a decision is a difficult, yet highly useful practice. No need to keep searching if info changes nothing!
3. One can ALWAYS randomize across a set of decisions as a convext combination of probabilities to get a desired expectation that is along a similar utility line. This usually doesn't change a given payoff for a choice in expectation for a rational agent, but if someone has different risk tolerances, it can allow for more optionality in a given situation.